/Amodal3Det

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images

By Zhuo Deng, Longin Jan Latecki (Temple University). This paper was published in CVPR 2017.

License

Code is released under the GNU GENERAL PUBLIC LICENSE (refer to the LICENSE file for details).

Cite The Paper

If you use this project for your research, please consider citing:

@inproceedings{zhuo17amodal3det,
    author = {Zhuo Deng and Longin Jan Latecki},
    booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
    title = {Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images},
    year = {2017}
}

Contents

  1. System requirements
  2. Basic Installation
  3. Usage

System requirements

The code is tested on the following system:

  1. OS: Ubuntu 14.04
  2. Hardware: Nvidia Titan X (GPU usage: ~9GB)
  3. Software: Caffe, CUDA-7.5, cuDNN v4, Matlab 2015a, Anaconda2

Basic Installation

  1. clone the Amodal3Det repository: 
    git clone https://github.com/phoenixnn/Amodal3Det.git
  2. build Caffe: 
    # assume you clone the repo into the local your_root_dir
cd your_root_dir
make all -j8 && make pycaffe
  3. install cuDNN: 
    sudo cp cudnn_folder/include/cudnn.h /usr/local/cuda-7.5/include/
sudo cp cudnn_folder/lib64/*.so* /usr/local/cuda-7.5/lib64/

Usage

  1. Download NYUV2 dataset with 3D annotations and unzip:

    wget 'https://cis.temple.edu/~latecki/TestData/DengCVPR2017/NYUV2_3D_dataset.zip' -P your_root_dir/dataset/NYUV2/

  2. Download precomputed 2D segment proposals based on MCG3D and unzip:

    wget 'https://cis.temple.edu/~latecki/TestData/DengCVPR2017/Segs.zip' -P your_root_dir/matlab/NYUV2/

  3. Download pretrained models and unzip:

    wget 'https://cis.temple.edu/~latecki/TestData/DengCVPR2017/pretrained.zip' -P your_root_dir/rgbd_3det/

    VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience.

  4. Run "your_root_dir/matlab/NYUV2/pipeline.m" in Matlab to extact required data.

  5. Set up training/test data:

    run "setup_training_data.py" and "setup_testing_data.py" under your_root_dir/rgbd_3det/data respectively

  6. Train model:

    cd your_root_dir
./trainNet.sh

  7. Test model: run "test_cnn.py"